[petsc-users] Fwd: Any changes in ML usage between 3.1-p8 -> 3.3-p6?

Mark F. Adams mark.adams at columbia.edu
Wed Apr 17 18:42:47 CDT 2013

In looking at the logs for icc it looks like Hong has done a little messing around with the shifting tolerance:

-  ((PC_Factor*)icc)->info.shiftamount = 1.e-12;
-  ((PC_Factor*)icc)->info.zeropivot   = 1.e-12;
+  ((PC_Factor*)icc)->info.shiftamount = 100.0*PETSC_MACHINE_EPSILON;
+  ((PC_Factor*)icc)->info.zeropivot   = 100.0*PETSC_MACHINE_EPSILON;

This looks like it would lower the shifting and drop tolerance.  You might set these back to 1e-12.  



BTW, using an indefinite preconditioner, that has to be fixed with is-this-a-small-number kind of code, on a warm and fluffy Laplacian is not recommended.  As I said before I would just use jacobi -- god gave you an easy problem.  Exploit it.

On Apr 17, 2013, at 7:22 PM, "Mark F. Adams" <mark.adams at columbia.edu> wrote:

> Begin forwarded message:
>> From: "Christon, Mark A" <christon at lanl.gov>
>> Subject: Re: [petsc-users] Any changes in ML usage between 3.1-p8 -> 3.3-p6?
>> Date: April 17, 2013 7:06:11 PM EDT
>> To: "Mark F. Adams" <mark.adams at columbia.edu>, "Bakosi, Jozsef" <jbakosi at lanl.gov>
>> Hi Mark,
>> Yes, looks like the new version does a little better after 2 iterations, but at the 8th iteration, the residuals increase:(
>> I suspect this is why PETSc is whining about an indefinite preconditioner.   
>> Something definitely changes as we've had about 6-8 regression tests start failing that have been running flawlessly with ML + PETSc 3.1-p8 for almost two years.
>> If we can understand what changed, we probably have a fighting chance of correcting it — assuming it's some solver setting for PETSc that we're not currently using.
>> - Mark
>> -- 
>> Mark A. Christon
>> Computational Physics Group (CCS-2)
>> Computer, Computational and Statistical Sciences Division
>> Los Alamos National Laboratory
>> MS D413, P.O. Box 1663
>> Los Alamos, NM 87545
>> E-mail: christon at lanl.gov
>> Phone: (505) 663-5124 
>> Mobile: (505) 695-5649 (voice mail)
>> International Journal for Numerical Methods in Fluids
>> From: "Mark F. Adams" <mark.adams at columbia.edu>
>> Date: Wed, 17 Apr 2013 18:51:02 -0400
>> To: PETSc users list <petsc-users at mcs.anl.gov>
>> Cc: "Mark A. Christon" <christon at lanl.gov>
>> Subject: Re: [petsc-users] Any changes in ML usage between 3.1-p8 -> 3.3-p6?
>>> I see you are using icc.  Perhaps our icc changed a bit between versions.  These results look like both solves are working and the old does a little better (after two iterations).
>>> Try using jacobi instead of icc.
>>> On Apr 17, 2013, at 6:21 PM, Jozsef Bakosi <jbakosi at lanl.gov> wrote:
>>>>> On 04.17.2013 15:38, Matthew Knepley wrote:
>>>>>> On 04.17.2013 14:26, Jozsef Bakosi wrote:
>>>>>>> Mark F. Adams mark.adams at columbia.edu
>>>>>>> Wed Apr 17 14:25:04 CDT 2013
>>>>>>> 2) If you get "Indefinite PC" (I am guessing from using CG) it is because the
>>>>>>> preconditioner
>>>>>>>     really is indefinite (or possible non-symmetric). We improved the checking
>>>>>>>     for this in one
>>>>>>>     of those releases.
>>>>>>> AMG does not guarantee an SPD preconditioner so why persist in trying to use
>>>>>>> CG?
>>>>>>> AMG is positive if everything is working correctly.
>>>>>>> Are these problems only semidefinite?  Singular systems can give erratic
>>>>>>> behavior.
>>>>>> It is a Laplace operator from Galerkin finite elements. And the PC is fine on
>>>>>> ranks 1, 2, 3, and 5 -- indefinite only on 4. I think we can safely say that the
>>>>>> same PC should be positive on 4 as well.
>>>>> Why is it safe? Because it sounds plausible? Mathematics is replete with things
>>>>> that sound plausible and are false. Are there proofs that suggest this? Is there
>>>>> computational evidence? Why would I believe you?
>>>> Okay, so here is some additional information:
>>>> I tried both old and new PETSc versions again, but now only taking 2 iterations
>>>> (both with 4 CPUs) and checked the residuals. I get the same exact PC from ML in
>>>> both cases, however, the residuals are different after both iterations:
>>>> Please do a diff on the attached files and you can verify that the ML
>>>> diagnostics are exactly the same: same max eigenvalues, nodes aggregated, etc,
>>>> while the norm coming out of the solver at the end at both iterations are
>>>> different.
>>>> We reproduced the same exact behavior on two different linux platforms.
>>>> Once again: same application source code, same ML source code, different PETSc:
>>>> 3.1-p8 vs. 3.3-p6.
>>>> <old.out><new.out>

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